ankitkushwaha90/Advanced_Rag_Lora_Finetune

Alright — let’s go deep into Advanced RAG (Retrieval-Augmented Generation) and Fine-Tuning with Transformers, with both theory and code so you’ll understand not just what to do, but why it works.

We’ll break this into 5 layers of understanding:

1. Advanced RAG: What It Is and Why It’s Used

Retrieval-Augmented Generation (RAG) is an LLM technique that:

Combines retrieval (from external data sources) with generation (from a transformer-based language model).
Allows your LLM to answer with knowledge it didn’t train on, while keeping the model small and up-to-date.
Reduces hallucinations by grounding responses in retrieved documents.

Pipeline:

Query → Embed Query → Vector Search → Retrieve Relevant Chunks → Context Merge → LLM Generates Answer

Core Components:

Document Store — FAISS, Milvus, Pinecone, Weaviate.
Embedding Model — e.g., sentence-transformers or OpenAI's text-embedding-ada-002.
Retriever — converts query to vector and finds top-k matches.

= Generator (LLM) — e.g., LLaMA-2, GPT, Mistral.

Advanced RAG vs Basic RAG:

Feature	Basic RAG	Advanced RAG
Retrieval	Static embeddings	Dynamic embeddings + query rewriting
Ranking	Vector similarity	Hybrid search (vector + keyword)
Context	Fixed-size chunk	Adaptive chunking & reranking
LLM Usage	Plain prompt	Structured prompts + reasoning

2. Advanced Fine-Tuning

Fine-tuning is different from RAG:

RAG: Doesn’t change model weights, adds external data at inference.
Fine-tuning: Updates model weights to adapt to your data.

Types of fine-tuning with Transformers:

Full fine-tuning — retrain all weights (costly).

LoRA (Low-Rank Adaptation) — add small trainable adapters to layers.
PEFT (Parameter-Efficient Fine-Tuning) — train small subset of weights.
Prefix Tuning / Prompt Tuning — learn continuous prompt vectors.

When to fine-tune instead of RAG:

Domain-specific language or style (e.g., medical reports).
Adding reasoning patterns.
Reducing prompt length for frequent queries.

3. Code: Advanced RAG with Transformers + FAISS

Here’s a minimal but advanced RAG pipeline using Hugging Face + FAISS + Transformers.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# 1. Load embedding model
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

# 2. Create FAISS index
dimension = embed_model.get_sentence_embedding_dimension()
index = faiss.IndexFlatL2(dimension)

# 3. Example documents
docs = [
    "RAG combines retrieval and generation to enhance LLM capabilities.",
    "Fine-tuning updates model weights to adapt to specific data.",
    "FAISS enables efficient similarity search for embeddings."
]

# 4. Embed documents and store in FAISS
doc_embeddings = embed_model.encode(docs)
index.add(np.array(doc_embeddings))

# 5. Load LLM
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large")
generator = pipeline("text2text-generation", model=model, tokenizer=tokenizer)

# 6. RAG query
def rag_query(query, top_k=2):
    query_vec = embed_model.encode([query])
    scores, ids = index.search(np.array(query_vec), top_k)
    retrieved = [docs[i] for i in ids[0]]
    
    context = "\n".join(retrieved)
    prompt = f"Answer based on the context:\n{context}\nQuestion: {query}"
    
    return generator(prompt, max_length=200)[0]['generated_text']

print(rag_query("What is the difference between RAG and fine-tuning?"))

4. Code: Fine-Tuning a Transformer with LoRA (PEFT)

Here’s LoRA-based fine-tuning for efficiency.

from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model
from datasets import load_dataset

# 1. Load dataset
dataset = load_dataset("json", data_files="train_data.json")

# 2. Load tokenizer and model
model_name = "EleutherAI/gpt-neo-1.3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 3. LoRA config
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)

# 4. Tokenize data
def tokenize(batch):
    return tokenizer(batch['text'], truncation=True, padding="max_length", max_length=512)

dataset = dataset.map(tokenize, batched=True)

# 5. Training setup
training_args = TrainingArguments(
    output_dir="./lora-finetuned",
    per_device_train_batch_size=2,
    num_train_epochs=3,
    logging_dir="./logs",
    logging_steps=10,
    save_strategy="epoch"
)

# 6. Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"]
)

trainer.train()

5. Combining Advanced RAG + Fine-Tuning

You can fine-tune your LLM for retrieval-augmented prompts:

Use LoRA to teach the LLM how to interpret retrieved chunks effectively.
Keep FAISS or Pinecone for dynamic retrieval.
This creates a retrieval-aware LLM — it won’t just parrot the chunks, but summarize, reason, and filter.

Workflow:

Build a RAG pipeline.
Log real user queries + retrieved documents + correct answers.
Fine-tune the LLM on these (instruction tuning).
Deploy with the same retrieval pipeline.

If you want, I can next write you a full working example of an advanced Retrieval-Aware Fine-Tuned Transformer, where LoRA fine-tuning is specifically trained to reason over FAISS-retrieved chunks. That’s the level where RAG + fine-tuning becomes enterprise-grade.

Do you want me to go ahead and make that combined advanced example?

ankitkushwaha90
/

Advanced_Rag_Lora_Finetune